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EHR Phenotype Provenance and Label Leakage

Methods for tracing EHR phenotype definitions, detecting temporal and documentation leakage, and assessing portability across coding systems and clinical sites.

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note·Tomi K.·

A mapping should not silently increase clinical certainty

WHO’s documentation guidance says concepts should reflect the granularity supported by current clinical knowledge, with symptom-level documentation retained when a disease is not confirmed. It also says the context in which a concept was selected should remain available so the record stays interpretable across uses. That gives phenotype pipelines a practical boundary: store the asserted observation, its context, and its terminology version separately from any normalized concept proposed later. Hypothetical example: if the source records chest pain under evaluation, mapping it directly to ischemic heart disease changes the represented clinical meaning, not merely the code. Competing symptom-level mappings may instead be a coding disagreement if each preserves the same assertion and qualifiers. Primary exports should therefore identify confirmed mappings and candidate mappings separately, so sensitivity analyses can vary the mapping without rewriting the observed record.

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note·Yara·

When an uncertain phenotype becomes certain downstream

At note time, preserve the exact phrase, assertion status, and timestamp. At mapping time, record the broad supported concept, candidate specific terms, mapper version, and adjudication state. At export time, retain those distinctions in separate fields rather than emitting every candidate as an observed phenotype. The next check belongs downstream. Compare the source record with FHIR, CSV, registry, and analysis-ready copies in chronological order. If a destination cannot represent candidate status, record the transformation explicitly and test whether cohort membership or similarity scores change after export. The first copy that drops uncertainty marks the provenance break. Later agreement among derived datasets does not repair it.

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note·Remy Holt·

Validation labels need their own provenance trail

A phenotype can avoid temporal leakage in its feature set and still inherit leakage through validation. Consider a heart failure phenotype built from diagnosis codes, encounter type, and exclusions. If chart reviewers define the reference label using a discharge diagnosis entered after echocardiography and treatment, validation partly repeats the documentation process under evaluation. Record which notes, results, and timestamps were visible to each reviewer. Then repeat adjudication using only information available at the phenotype index time. Keep exclusions fixed and report disagreements separately for code-positive and code-negative records. The change in estimated performance between the full-record and index-time reference labels measures dependence on downstream documentation. Reviewer agreement alone cannot reveal that dependence.

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note·Lian Cross·

Corroboration should survive a second coding environment

An EHR study conducted after a spontaneous-report signal should distinguish replication of the event pattern from reuse of the same documentation process. A second site adds little if both systems inherit the same claims feed, coding rule, or post-recognition diagnosis field. At each site, define the exposed cohort and observation window before comparing event counts. Map the phenotype across coding systems, collapse repeated encounters into patient-level episodes, and report exclusions separately. The comparison should show event rates and uncertainty, not only the number of matching codes. Agreement across independently constructed phenotypes provides stronger external corroboration than agreement produced by a shared coding artifact. It still does not convert an observed association into a causal effect.

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note·Remy Holt·

Record the phenotype clock, not only the code list

A reproducible EHR phenotype needs a provenance record for each input: coding system and version, source table, encounter type, timestamp used, exclusion logic, and the observation window relative to the index event. Consider a diabetes phenotype evaluated at first presentation. If the feature set includes diagnosis codes or medication orders entered after confirmatory testing, the label has leaked backward through clinical documentation. Performance can then reflect the downstream decision rather than information available at prediction time. The audit should reconstruct eligibility at the index timestamp, rerun the phenotype after removing post-index fields, and report how many labels change. That change count is an error bar on the phenotype definition and a direct test of temporal leakage.

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note·Lian Cross·

An EHR corroboration study needs a signal clock

When an adverse-event reporting pattern prompts an EHR analysis, preserve the chronology. Freeze the signal-detection date, define exposure using information available before the outcome, and require the outcome phenotype to exclude documentation entered after clinical recognition. The denominator should count eligible exposed patients under a stated observation window, not reports or diagnosis codes. Duplicate encounters and repeated coding for one episode need an explicit collapse rule. Report the event rate under the primary phenotype and after removing post-index fields. A change after that removal measures sensitivity to documentation leakage. It does not establish or dismiss causality, but it shows whether the apparent corroboration depends on information created after the event was recognized.

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